These 10 Free Machine Learning courses will make you an ML Expert

Machine Learning (ML) has become one of the most revolutionary technologies of our time, driving innovations across various industries, from healthcare and finance to marketing and entertainment. As the demand for ML experts continues to soar, acquiring expertise in this field has never been more crucial. Fortunately, there is a plethora of online resources available, including free courses that can help you embark on your journey to becoming an ML expert. In this article, we present ten of the best free machine learning courses that will equip you with the skills and knowledge to excel in this exciting domain.

1. Machine Learning Crash Course(Google)

Google’s Machine Learning Crash Course is a free, self-paced online course that teaches you the basics of machine learning. The course covers a wide range of topics, including:

• What is machine learning?
• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Natural language processing
• Computer vision
• Deep learning

The course is divided into 10 modules, each of which includes video lectures, interactive exercises, and real-world case studies. The exercises are designed to help you apply the concepts you learn in the lectures, and the case studies give you a glimpse of how machine learning is used in the real world.

The course is aimed at beginners who want to learn the basics of machine learning. No prior experience with machine learning is required. However, some familiarity with programming is helpful.

The course is taught by Andrew Ng, a professor of computer science at Stanford University and the co-founder of Coursera. Ng is a leading expert in machine learning, and his lectures are clear, engaging, and informative.

The Machine Learning Crash Course is a great way to learn the basics of machine learning. It is well-organized, comprehensive, and engaging. If you are interested in learning about machine learning, I highly recommend this course.

Here are some additional details about the course:

• The course is free to enroll in.
• The course is self-paced, so you can work at your own pace.
• The course is available online, so you can take it from anywhere.
• The course is taught in English.

2. Machine Learning Specialization by Andrew Ng(Coursera)

The Machine Learning Specialization by Andrew Ng is a 3-course specialization offered by Coursera. It is a beginner-level program aimed at those new to AI and looking to gain a foundational understanding of machine learning models and real-world experience building systems using Python.

The specialization covers the following topics:

• Supervised Learning: This course covers the fundamentals of supervised learning, including linear regression, logistic regression, decision trees, and support vector machines.
• Unsupervised Learning: This course covers the fundamentals of unsupervised learning, including clustering, dimensionality reduction, and anomaly detection.
• Reinforcement Learning: This course covers the fundamentals of reinforcement learning, including Markov decision processes, Q-learning, and policy gradients.

Each course in the specialization includes video lectures, interactive exercises, and real-world case studies. The exercises are designed to help you apply the concepts you learn in the lectures, and the case studies give you a glimpse of how machine learning is used in the real world.

The specialization is taught by Andrew Ng, a pioneer in the field of machine learning. Ng is the co-founder of Coursera and the former chief scientist at Baidu. He is also the author of the popular book “Machine Learning: A Probabilistic Perspective.”

The Machine Learning Specialization is a great way to learn the basics of machine learning. It is well-organized, comprehensive, and engaging. If you are interested in learning about machine learning, I highly recommend this specialization.

Here are some additional details about the specialization:

• The specialization is offered on Coursera, which is a leading online learning platform.
• The specialization is self-paced, so you can work at your own pace.
• The specialization is available in English

3. Machine Learning by Columbia University (edX)

The Machine Learning course by Columbia University on edX is a beginner-level course that teaches you the basics of machine learning. The course covers a wide range of topics, including:

• What is machine learning?
• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Natural language processing
• Computer vision
• Deep learning

The course is divided into 7 modules, each of which includes video lectures, interactive exercises, and real-world case studies. The exercises are designed to help you apply the concepts you learn in the lectures, and the case studies give you a glimpse of how machine learning is used in the real world.

The course is aimed at beginners who want to learn the basics of machine learning. No prior experience with machine learning is required. However, some familiarity with programming is helpful.

The course is taught by professors from Columbia University, who are experts in machine learning. The lectures are clear, engaging, and informative.

The Machine Learning course by Columbia University is a great way to learn the basics of machine learning. It is well-organized, comprehensive, and engaging. If you are interested in learning about machine learning, I highly recommend this course.

Here are some additional details about the course:

• The course is offered on edX, which is a leading online learning platform.
• The course is self-paced, so you can work at your own pace.
• The course is available in English.

4. Introduction to Machine Learning for Coders by fast.ai

The Introduction to Machine Learning for Coders course by fast.ai is a beginner-level course that teaches you the basics of machine learning using Python. The course covers a wide range of topics, including:

• What is machine learning?
• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Natural language processing
• Computer vision
• Deep learning

The course is divided into 12 lessons, each of which is around two hours long. The lessons are taught by Jeremy Howard, a leading expert in machine learning. Howard is the co-founder of fast.ai and the former president of Kaggle.

The course is aimed at beginners who want to learn the basics of machine learning. No prior experience with machine learning is required. However, some familiarity with programming is helpful.

The course is taught using the fast.ai library, which is a high-level library that makes it easy to build and train machine learning models. The library is based on PyTorch, which is a popular deep learning framework.

The Introduction to Machine Learning for Coders course is a great way to learn the basics of machine learning. It is well-organized, comprehensive, and engaging. If you are interested in learning about machine learning, I highly recommend this course.

Here are some additional details about the course:

• The course is offered on fast.ai, which is a leading online learning platform.
• The course is self-paced, so you can work at your own pace.
• The course is available in English.

Here are some of the benefits of taking this course:

• You will learn the basics of machine learning from a leading expert in the field.
• You will learn how to use the fast.ai library to build and train machine learning models.
• You will learn how to apply machine learning to real-world problems.
• You will gain a strong foundation in machine learning that will allow you to continue your learning in this field.

5. Python for Data Science and Machine Learning Bootcamp (Udemy)

The Python for Data Science and Machine Learning Bootcamp (Udemy) is a comprehensive course that teaches you the basics of Python for data science and machine learning. The course covers a wide range of topics, including:

• Basic Python syntax
• Data structures and algorithms
• Object-oriented programming
• NumPy
• Pandas
• Matplotlib
• Scikit-learn
• Machine learning algorithms

The course is taught by Kirill Eremenko, a leading expert in data science and machine learning. Eremenko is the co-founder of DataCamp and the author of the popular book "Python for Data Science."

The course is aimed at beginners who want to learn Python for data science and machine learning. No prior experience with Python is required. However, some familiarity with programming is helpful.

The course is well-organized, comprehensive, and engaging. The lectures are clear and easy to follow, and the exercises are challenging but rewarding. The course also includes a number of real-world projects that you can work on to apply what you have learned.

Here are some of the benefits of taking this course:

• You will learn the basics of Python for data science and machine learning from a leading expert in the field.
• You will learn how to use Python to analyze data, build machine learning models, and create visualizations.
• You will learn how to apply your skills to real-world problems.
• You will gain a strong foundation in Python for data science and machine learning that will allow you to continue your learning in this field.

If you are interested in learning Python for data science and machine learning, I highly recommend this course.

Here are some additional details about the course:

• The course is offered on Udemy, which is a leading online learning platform.
• The course is self-paced, so you can work at your own pace.
• The course is available in English.

6. Mathematics for Machine Learning by Imperial College London (Coursera)

The Mathematics for Machine Learning Specialization by Imperial College London on Coursera is a 3-course specialization that teaches you the mathematical foundations of machine learning. The specialization covers the following topics:

• Linear Algebra: This course covers the fundamentals of linear algebra, including vectors, matrices, and eigenvalues.
• Multivariate Calculus: This course covers the fundamentals of multivariate calculus, including gradients, optimization, and least squares.
• Probability and Statistics: This course covers the fundamentals of probability and statistics, including distributions, hypothesis testing, and machine learning.

Each course in the specialization includes video lectures, interactive exercises, and real-world case studies. The exercises are designed to help you apply the concepts you learn in the lectures, and the case studies give you a glimpse of how machine learning is used in the real world.

The specialization is taught by professors from Imperial College London, who are experts in machine learning. The lectures are clear, engaging, and informative.

The Mathematics for Machine Learning Specialization is a great way to learn the mathematical foundations of machine learning. It is well-organized, comprehensive, and engaging. If you are interested in learning about machine learning, I highly recommend this specialization.

Here are some additional details about the specialization:

• The specialization is offered on Coursera, which is a leading online learning platform.
• The specialization is self-paced, so you can work at your own pace.
• The specialization is available in English.

If you are interested in learning more about the specialization, you can visit the following website:

Here are some of the benefits of taking this specialization:

• You will learn the mathematical foundations of machine learning from experts in the field.
• You will learn how to apply mathematical concepts to machine learning problems.
• You will gain a strong foundation in mathematics that will allow you to continue your learning in this field.

7. Applied Machine Learning in Python by University of Michigan (Coursera)

The Applied Machine Learning in Python course by University of Michigan on Coursera is a beginner-level course that teaches you the basics of machine learning using Python. The course covers a wide range of topics, including:

• What is machine learning?
• Supervised learning
• Unsupervised learning
• Dimensionality reduction
• Clustering
• Model evaluation
• Feature engineering
• Natural language processing
• Computer vision

The course is divided into 4 modules, each of which includes video lectures, interactive exercises, and real-world case studies. The exercises are designed to help you apply the concepts you learn in the lectures, and the case studies give you a glimpse of how machine learning is used in the real world.

The course is aimed at beginners who want to learn the basics of machine learning. No prior experience with machine learning is required. However, some familiarity with programming is helpful.

The course is taught by professors from the University of Michigan, who are experts in machine learning. The lectures are clear, engaging, and informative.

The Applied Machine Learning in Python course is a great way to learn the basics of machine learning. It is well-organized, comprehensive, and engaging. If you are interested in learning about machine learning, I highly recommend this course.

Here are some additional details about the course:

• The course is offered on Coursera, which is a leading online learning platform.
• The course is self-paced, so you can work at your own pace.
• The course is available in English.

Here are some of the benefits of taking this course:

• You will learn the basics of machine learning from experts in the field.
• You will learn how to use Python to build and train machine learning models.
• You will learn how to apply machine learning to real-world problems.
• You will gain a strong foundation in machine learning that will allow you to continue your learning in this field

8. Data Science and Machine Learning Bootcamp with R (Udemy)

The Data Science and Machine Learning Bootcamp with R (Udemy) is a comprehensive course that teaches you the basics of data science and machine learning using R. The course covers a wide range of topics, including:

• Introduction to R: This section covers the basics of R, including variables, data types, functions, and loops.
• Data Cleaning and Preparation: This section covers how to clean and prepare data for analysis, including dealing with missing values, outliers, and categorical data.
• Data Visualization: This section covers how to create data visualizations using R, including bar charts, line graphs, and heatmaps.
• Statistical Modeling: This section covers how to build statistical models using R, including linear regression, logistic regression, and decision trees.
• Machine Learning: This section covers how to build machine learning models using R, including support vector machines, random forests, and neural networks.

The course is taught by Jose Portilla, a leading expert in data science and machine learning. Portilla is the author of the popular book “R for Data Science” and the founder of DataCamp.

The course is aimed at beginners who want to learn data science and machine learning using R. No prior experience with R is required. However, some familiarity with programming is helpful.

The course is well-organized, comprehensive, and engaging. The lectures are clear and easy to follow, and the exercises are challenging but rewarding. The course also includes a number of real-world projects that you can work on to apply what you have learned.

Here are some of the benefits of taking this course:

• You will learn the basics of data science and machine learning from a leading expert in the field.
• You will learn how to use R to analyze data, build models, and create visualizations.
• You will learn how to apply your skills to real-world problems.
• You will gain a strong foundation in data science and machine learning that will allow you to continue your learning in this field.

If you are interested in learning data science and machine learning using R, I highly recommend this course.

Here are some additional details about the course:

• The course is offered on Udemy, which is a leading online learning platform.
• The course is self-paced, so you can work at your own pace.
• The course is available in English.

9. TensorFlow in Practice by deeplearning.ai (Coursera)

The TensorFlow in Practice Specialization by deeplearning.ai on Coursera is a 4-course specialization that teaches you how to use TensorFlow to build and train machine learning models. The specialization covers the following topics:

• Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning: This course covers the basics of TensorFlow, including tensors, variables, operations, and gradients.
• Convolutional Neural Networks in TensorFlow: This course covers how to build convolutional neural networks (CNNs) using TensorFlow. CNNs are a type of neural network that are commonly used for image recognition and computer vision tasks.
• Natural Language Processing in TensorFlow: This course covers how to build natural language processing (NLP) models using TensorFlow. NLP models are used to understand and process human language.
• Sequences, Time Series and Prediction: This course covers how to build sequence models using TensorFlow. Sequence models are used to predict future values in a sequence, such as stock prices or weather patterns.

Each course in the specialization includes video lectures, interactive exercises, and real-world case studies. The exercises are designed to help you apply the concepts you learn in the lectures, and the case studies give you a glimpse of how TensorFlow is used in the real world.

The specialization is taught by experts from deeplearning.ai, who are leaders in the field of machine learning. The lectures are clear, engaging, and informative.

The TensorFlow in Practice Specialization is a great way to learn how to use TensorFlow to build and train machine learning models. It is well-organized, comprehensive, and engaging. If you are interested in learning about TensorFlow, I highly recommend this specialization.

Here are some additional details about the specialization:

• The specialization is offered on Coursera, which is a leading online learning platform.
• The specialization is self-paced, so you can work at your own pace.
• The specialization is available in English.

Here are some of the benefits of taking this specialization:

• You will learn how to use TensorFlow to build and train machine learning models.
• You will learn how to apply your skills to real-world problems.
• You will gain a strong foundation in TensorFlow that will allow you to continue your learning in this field.

10. Introduction to Deep Learning with PyTorch by DeepAI (Coursera)

The Introduction to Deep Learning with PyTorch course by DeepAI on Coursera is a beginner-level course that teaches you how to use PyTorch to build and train deep learning models. The course covers the following topics:

• What is deep learning?
• Tensors and automatic differentiation
• Linear regression and logistic regression
• Feedforward neural networks
• Convolutional neural networks
• Recurrent neural networks
• Transfer learning

The course is divided into 10 modules, each of which includes video lectures, interactive exercises, and real-world case studies. The exercises are designed to help you apply the concepts you learn in the lectures, and the case studies give you a glimpse of how deep learning is used in the real world.

The course is taught by experts from DeepAI, who are leaders in the field of deep learning. The lectures are clear, engaging, and informative.

The Introduction to Deep Learning with PyTorch course is a great way to learn how to use PyTorch to build and train deep learning models. It is well-organized, comprehensive, and engaging. If you are interested in learning about deep learning, I highly recommend this course.

Here are some additional details about the course:

• The course is offered on Coursera, which is a leading online learning platform.
• The course is self-paced, so you can work at your own pace.
• The course is available in English.

Here are some of the benefits of taking this course:

• You will learn how to use PyTorch to build and train deep learning models.
• You will learn how to apply your skills to real-world problems.
• You will gain a strong foundation in PyTorch that will allow you to continue your learning in this field.

In conclusion, these ten free machine learning courses provide a fantastic opportunity to start your journey toward becoming an ML expert. Whether you are a complete beginner or an experienced programmer looking to specialize in ML, these courses offer a diverse range of learning materials, practical exercises, and real-world projects. Remember, consistency and hands-on practice are key to mastering machine learning, so dive in, stay persistent, and let these courses be your guiding light in the fascinating world of machine learning. Happy learning!